@book{陈德刚2019粒计算基础教程,
  title     = {粒计算基础教程},
  author    = {陈德刚 and 徐伟华 and 李金海},
  publisher = {粒计算基础教程},
  year      = {2019}
}

@book{张文修2003信息系统与知识发现,
  title     = {信息系统与知识发现},
  author    = {张文修 and 梁怡 and 吴伟志},
  publisher = {信息系统与知识发现},
  year      = {2003}
}


@article{bezdek_fcm_1984,
  title        = {{FCM}: The fuzzy c-means clustering algorithm},
  volume       = {10},
  rights       = {https://www.elsevier.com/tdm/userlicense/1.0/},
  issn         = {00983004},
  doi          = {10.1016/0098-3004(84)90020-7},
  shorttitle   = {{FCM}},
  pages        = {191--203},
  number       = {2},
  journal      = {Computers \& Geosciences},
  shortjournal = {Computers \& Geosciences},
  author       = {Bezdek, James C. and Ehrlich, Robert and Full, William},
  urldate      = {2024-07-05},
  year         = {1984-01},
  langid       = {english}
}


@article{yu_frcm_2024,
  title        = {{FRCM}: A fuzzy rough c-means clustering method},
  volume       = {480},
  issn         = {01650114},
  doi          = {10.1016/j.fss.2024.108860},
  shorttitle   = {{FRCM}},
  pages        = {108860},
  journaltitle = {Fuzzy Sets and Systems},
  journal      = {Fuzzy Sets and Systems},
  author       = {Yu, Bin and Zheng, Zijian and Cai, Mingjie and Pedrycz, Witold and Ding, Weiping},
  urldate      = {2024-07-04},
  year         = {2024-03},
  langid       = {english}
}

@article{stetco_fuzzy_2015,
  title        = {Fuzzy C-means++: Fuzzy C-means with effective seeding initialization},
  volume       = {42},
  issn         = {09574174},
  doi          = {10.1016/j.eswa.2015.05.014},
  shorttitle   = {Fuzzy C-means++},
  pages        = {7541--7548},
  number       = {21},
  journal      = {Expert Systems with Applications},
  shortjournal = {Expert Systems with Applications},
  author       = {Stetco, Adrian and Zeng, Xiao-Jun and Keane, John},
  urldate      = {2024-07-04},
  year         = {2015-11},
  langid       = {english}
}

@book{邢文训2005现代优化计算方法,
  title     = {现代优化计算方法.第2版},
  author    = {邢文训 and 谢金星},
  publisher = {现代优化计算方法.第2版},
  year      = {2005}
}

@article{10499845,
  author   = {Wang, Changzhong and Wang, Changyue and Qian, Yuhua and Leng, Qiangkui},
  journal  = {IEEE Transactions on Fuzzy Systems},
  title    = {Feature Selection Based on Weighted Fuzzy Rough Sets},
  year     = {2024},
  volume   = {32},
  number   = {7},
  pages    = {4027-4037},
  keywords = {Rough sets;Feature extraction;Fuzzy sets;Data models;Approximation algorithms;Weight measurement;Fuzzy systems;Feature selection;fuzzy rough set;sample discrimination;weighted fuzzy approximation operator (WFAO)},
  doi      = {10.1109/TFUZZ.2024.3387571}
}
@article{2004A,
  author     = {Luxburg, Ulrike},
  title      = {A tutorial on spectral clustering},
  year       = {2007},
  issue_date = {December  2007},
  publisher  = {Kluwer Academic Publishers},
  address    = {USA},
  volume     = {17},
  number     = {4},
  issn       = {0960-3174},
  doi        = {10.1007/s11222-007-9033-z},
  journal    = {Statistics and Computing},
  month      = {dec},
  pages      = {395–416},
  numpages   = {22},
  keywords   = {Spectral clustering, Graph Laplacian}
}
@article{Helmut1996Handbook,
  title   = {Handbook of matrices},
  author  = {Helmut Lütkepohl},
  journal = {handbook of matrices},
  year    = {1996}
}
@article{CHEN2024109134,
  title    = {Feature selection of dominance-based neighborhood rough set approach for processing hybrid ordered data},
  journal  = {International Journal of Approximate Reasoning},
  volume   = {167},
  pages    = {109134},
  year     = {2024},
  issn     = {0888-613X},
  doi      = {https://doi.org/10.1016/j.ijar.2024.109134},
  author   = {Jiayue Chen and Ping Zhu},
  keywords = {Hybrid ordered data, Dominance-based rough set, Neighborhood, Feature selection, Discernibility matrix},
  abstract = {Feature selection is a fundamental application of rough set theory in identifying significant features and reducing data dimensionality. For ordered data (OD), existing studies of feature selection mainly aim at ODs with specific criteria, i.e., single-valued, interval-valued, or set-valued criteria. However, these studies are inapplicable to ODs simultaneously including the three criteria, namely, hybrid ODs (HODs). To fill such a gap, this paper investigates feature selection of HODs using dominance-based neighborhood rough sets (DNRSs). Firstly, we introduce a kind of DNRS model for HODs, examine its properties, and establish its relationships with other dominance-based rough sets. Corresponding to DNRSs of two different target concepts in HODs, we propose feature selections based on approximation accuracies, and the two feature selections are proven to be equivalent by the complementarity property of DNRSs. For the computation of the proposed feature selection, we construct discernibility criterion set, which is then employed to define the family of approximation discernibility criterion sets (ADCSF) and its minimal description (MD-ADCSF). All reducts and the most discriminative reduct are computed through MD-ADCSF, and the algorithms of MD-ADCSF and the most discriminative reduct are achieved in matrix form. Finally, we verify validity and effectiveness of the two algorithms by comparison experiments on nine real UCI datasets.}
}

@article{2019Granular,
  title   = {Granular Ball Computing Classifiers for Efficient, Scalable and Robust Learning},
  author  = { Xia, Shuyin  and  Liu, Yunsheng  and  Ding, Xin  and  Wang, Guoyin  and  Luo, Yuoguo },
  journal = {Information Sciences},
  volume  = {483},
  year    = {2019},
  doi     = {10.1016/j.ins.2019.01.010}
}